[hal-01078378] Unveiling Contacts within Macro-molecular assemblies by solving Minimum Weight Connectivity Inference Problems

Consider a set of oligomers listing the subunits involved in sub-complexes of a macro-molecular assembly obtained eg using native mass spectrometry or affinity purification Given these oligomers connectivity inference CI consists of finding the most plausible contacts between these subunits and minimum connectivity inference MCI is the variant consisting of finding a set of contacts of smallest cardinality MCI problems avoid speculating on the total number of contacts but yield a subset of all contacts and do not allow exploiting a priori information on the likelihood of individual contacts In this context we present two novel algorithms ALGO-MILP-W and ALGO-MILP-WB The former solves the minimum weight connectivity inference MWCI an optimization problem whose criterion mixes the number of contacts and their likelihood The latter uses the former in a bootstrap fashion to improve the sensitivity and the specificity of solution sets Experiments on the yeast exosome for which both a high resolution crystal structure and a large set of oligomers is known show that our algorithms predict contacts with high specificity and sensitivity yielding a very significant improvement over previous work The software accompanying this paper is made available and should prove of ubiquitous interest whenever connectivity inference from oligomers is faced



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